Snapshot management architecture for process control operator training system lifecycle

ABSTRACT

A cloud-based operator training system includes a snapshot management architecture, which provides a hybrid system for generation of control system level scenarios and system-state snapshots, and which can improve the fidelity of a training simulation. By implementing the simulation system on a cloud platform, the system can generate a large and growing set of snapshot files representing various control states and corresponding process states. These files can then be leverage during operator training sessions to yield high fidelity simulated system operation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Utility application Ser. No.14/987,144, filed on Jan. 4, 2016, and entitled “SNAPSHOT MANAGEMENTARCHITECTURE FOR PROCESS CONTROL OPERATOR TRAINING SYSTEM LIFECYCLE”,which claimed priority to U.S. Provisional Application Ser. No.62/196,230, filed on Jul. 23, 2015, and entitled “SNAPSHOT MANAGEMENTARCHITECTURE FOR PROCESS CONTROL OPERATOR TRAINING SYSTEM LIFECYCLE,”and to the entirety of both which are incorporated herein by reference.

BACKGROUND

The subject matter disclosed herein relates generally to industrialsimulations, and, for example, to a cloud-based operator training system

BRIEF DESCRIPTION

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is it intended to identify key/critical elementsor to delineate the scope of the various aspects described herein. Itssole purpose is to present some concepts in a simplified form as aprelude to the more detailed description that is presented later.

In one or more embodiments, a system is provided, comprising anemulation component configured to execute a virtualized industrialcontroller on a cloud platform; a simulation component configured toexecute a simulation of an industrial system on the cloud platform; anemulation data exchange component configured to execute an emulationdata exchange interface that communicatively connects the virtualizedcontroller and the simulation; and a snapshot management componentconfigured to capture process state data representing a process state ofthe simulation and control state data representing a control state ofthe virtualized industrial controller in response to a determinationthat a defined condition of the simulation has been satisfied, and togenerate a snapshot file based on the process state data and the controlstate data that records the process state and the control state.

Also, one or more embodiments provide a method for industrial operatortraining, comprising executing, by one or more cloud platform devicescomprising at least one processor, a virtualized industrial controller;executing, by the one or more cloud platform devices, a simulation of anindustrial system; executing, by the one or more cloud platform devices,an emulation data exchange interface that communicatively connects thevirtualized controller and the simulation; determining, by the one ormore cloud platform devices, that a defined condition of the simulationhas been satisfied; recording, by the one or more cloud platform devicesin response to the determining, process state data representing aprocess state of the simulation and control state data representing acontrol state of the virtualized industrial controller; and storing, bythe one or more cloud platform devices, a snapshot file based on theprocess state data and the control state data that records the processstate and the control state.

Also, according to one or more embodiments, a non-transitorycomputer-readable medium is provided having stored thereon executableinstructions that, in response to execution, cause a system to performoperations, the operations comprising executing a virtualized industrialcontroller on a cloud platform; executing a simulation of an industrialsystem on the cloud platform; executing an emulation data exchangeinterface on the cloud platform, the emulation data exchange interfacefacilitating data exchange between the virtualized controller and thesimulation; detecting that a defined condition of the simulation hasbeen satisfied; recording, in response to the detecting, process statedata representing a process state of the simulation and control statedata representing a control state of the virtualized industrialcontroller; and storing a snapshot file comprising the process statedata and the control state data, the snapshot file recording the processstate and the control state.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram illustrating an example automatedindustrial process controlled by an industrial controller.

FIG. 2 is block diagram of an example industrial simulation system.

FIG. 3 is a diagram illustrating configuration of plant-level andcloud-level modeling and simulation systems.

FIG. 4 is a high-level overview of an industrial enterprise thatleverages cloud-based services.

FIG. 5 is a block diagram of an example cloud-based operator trainingsystem.

FIG. 6 is an overview of a system that leverages an agent-based cloudinfrastructure to provide data collection and processing services tocustomer manufacturing sites.

FIG. 7 is a block diagram illustrating cloud agent functionality.

FIG. 8 is an example compressed data packet.

FIG. 9 is a diagram illustrating runtime of a cloud-based simulation andemulation system that includes a snapshot management component forcreation of operator training scenarios.

FIG. 10 is a diagram illustrating generation and storage of snapshotfiles by snapshot management component.

FIG. 11 is a flow diagram illustrating an example sequence of eventscarried out by an OTS system for generating and storing snapshot files.

FIG. 12 is a diagram of an architecture for distributing OTS interfacesto user workstations to facilitate training interaction with acloud-based simulation.

FIG. 13 is a diagram illustrating exchange of OTS data 1302 between thecloud-based OTS system and workstation.

FIG. 14 an example, non-limiting organizational schema forindustry-based categorization of snapshot files.

FIG. 15 an example computing environment.

FIG. 16 is an example networking environment

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “controller,” “terminal,” “station,” “node,”“interface” are intended to refer to a computer-related entity or anentity related to, or that is part of, an operational apparatus with oneor more specific functionalities, wherein such entities can be eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical or magnetic storage medium)including affixed (e.g., screwed or bolted) or removable affixedsolid-state storage drives; an object; an executable; a thread ofexecution; a computer-executable program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or Application Programming Interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,platform, interface, layer, controller, terminal, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

Industrial controllers and their associated I/O devices are central tothe operation of modern automation systems. FIG. 1 is a simplifieddiagram illustrating an example automated industrial process controlledby an industrial controller 102. Industrial controller 102 interactswith industrial devices 104 on the plant floor to control one or moreautomated processes relating to such objectives as product manufacture,material handling, batch processing, supervisory control, and other suchapplications. Industrial controller 102 stores and executes auser-defined control program 106 to effect decision-making in connectionwith the controlled process. Such programs can include, but are notlimited to, ladder logic, sequential function charts, function blockdiagrams, structured text, or other such programming structures.

Many system designers use simulations of a plant or industrial system tovalidate an industrial control program prior to deployment, to predictthe effects of a change to the industrial process or control program(e.g., to estimate the change in product yield, energy or materialconsumption, costs, etc.). FIG. 2 is block diagram of an exampleindustrial simulation system 202. Industrial simulation system 202includes a system simulation component 204 that executes an industrialsystem simulation 206, which simulates various aspects of a physicalindustrial automation system or process to be regulated by an industrialcontroller. Industrial simulation system 202 also includes a controlprogram emulation component 210 that executes an emulated controlprogram 212 analogous to the industrial control program that will beinstalled and executed on the physical industrial controller used tomonitor and control the real industrial automation system. Theindustrial system simulation 206 interfaces with the emulated controlprogram 212 to exchange simulated I/O data (e.g., controller output data208 and simulation output data 214), thereby simulating real-timecontrol of the industrial system or process. For example, if theindustrial system simulation 206 comprises a model of an industrialrobot arm, the emulated control program 212 can be configured to managemovement of the arm.

Enterprise-level modeling and simulation of an industrial automationsystem, or even a distributed industrial enterprise comprising multipleinter-related facilities, could be achieved by modeling and simulatingthe systems in a cloud platform. FIG. 3 is a diagram illustratingconfiguration of plant-level and cloud-level modeling and simulationsystems. In this example, a modeling and simulation system 310 on theplant level interacts with an on-premise industrial controller312—either before deployment of the controller or during control of anindustrial system 316—to perform system validation, modeling, analytics,operator training, or other functions. Modeling and simulation system310 may execute, for example, on a workstation or simulation moduleresiding on the plant floor. Similarly, a cloud-based modeling andsimulation system 306 executing on a cloud platform may leverage datamaintained in cloud storage 304—e.g., historical and/or near real-timedata collected from one or more industrial devices—to facilitatesimulation of an industrial system or process on the cloud. These cloudand on-premise simulation systems are decoupled, requiring separateconfiguration and software development tools to achieve enterprise levelmodeling of an industrial system. For example, modeling and simulationsystem 310 and industrial controller 312 may be configured usingon-premise configuration tools 314, which may comprise industrialcontrol program development software (e.g., a ladder logic developmentplatform) and simulation building software. Cloud-based modeling andsimulation system 306, which executes on a different type of platform inthe cloud, must be configured using a different set of cloudconfiguration tools 308 not familiar to an industrial control systemdesigner. Often, cloud-based analytics systems must be configured bydata scientists with expertise in big data analytics.

Cloud-based industrial simulation systems would have considerable valuein connection with operator training systems (OTSs). Such systems couldeffectively leverage the high performance and storage capabilitiesafforded by the cloud platform in an integrated OTS framework thataccurately reproduces control and process scenarios for a givenreal-world industrial automation system. Operators and/or maintenancepersonnel could then interact with these simulated scenarios within thecontext of a training scenario. In general, operator training systemscan be used to test the design of a new industrial automation systemwithout putting the physical system at risk, to train new personnel inthe proper operation of an industrial automation system, or to transferdomain expertise to new operators and engineers. Typically, operatortraining systems are based on ideal simulated versions of the controlledsystem or process. These simulations often require ad-hoc programmingand customization to align the simulation as closely as possible to thephysical system. As such, operator training systems created in this wayare often complicated, monolithic, expensive, and do not accuratelyreproduce the behavior of the physical system with high fidelity.

To address these and other issues, one or more embodiments of thepresent disclosure provide a cloud-based operator training system thatincludes a snapshot management architecture. The snapshot managementarchitecture provides a hybrid system for the generation of controlsystem level scenarios and system-state snapshots, which can improve thefidelity of the training simulation. By implementing the simulationsystem on a cloud platform, the system can generate a large and growingset of snapshot files representing various control states andcorresponding process states. These files can then be leveraged duringoperator training sessions to yield high fidelity simulated systemoperation.

FIG. 4 illustrates a high-level overview of an industrial enterprisethat leverages cloud-based services. The enterprise comprises one ormore industrial facilities 404, each having a number of industrialdevices 408 and 410 in use. The industrial devices 408 and 410 can makeup one or more automation systems operating within the respectivefacilities 404. Example automation systems can include, but are notlimited to, batch control systems (e.g., mixing systems), continuouscontrol systems (e.g., PID control systems), or discrete controlsystems. Industrial devices 408 and 410 can include such devices asindustrial controllers (e.g., programmable logic controllers or othertypes of programmable automation controllers); field devices such assensors and meters; motor drives; operator interfaces (e.g.,human-machine interfaces, industrial monitors, graphic terminals,message displays, etc.); industrial robots, barcode markers and readers;vision system devices (e.g., vision cameras); smart welders; or othersuch industrial devices.

Example automation systems can include one or more industrialcontrollers that facilitate monitoring and control of their respectiveprocesses. The controllers exchange data with the field devices usingnative hardwired I/O or via a plant network such as Ethernet/IP, DataHighway Plus, ControlNet, Devicenet, or the like. A given controllertypically receives any combination of digital or analog signals from thefield devices indicating a current state of the devices and theirassociated processes (e.g., temperature, position, part presence orabsence, fluid level, etc.), and executes a user-defined control programthat performs automated decision-making for the controlled processesbased on the received signals. The controller then outputs appropriatedigital and/or analog control signaling to the field devices inaccordance with the decisions made by the control program. These outputscan include device actuation signals, temperature or position controlsignals, operational commands to a machining or material handling robot,mixer control signals, motion control signals, and the like. The controlprogram can comprise any suitable type of code used to process inputsignals read into the controller and to control output signals generatedby the controller, including but not limited to ladder logic, sequentialfunction charts, function block diagrams, structured text, or other suchplatforms.

Although the example overview illustrated in FIG. 4 depicts theindustrial devices 408 and 410 as residing in fixed-location industrialfacilities 404, the industrial devices 408 and 410 may also be part of amobile control application, such as a system contained in a truck orother service vehicle.

According to one or more embodiments, on-premise cloud agents 406 cancollect data from industrial devices 408 and 410—or from other datasources, including but not limited to data historians, business-levelsystems, etc.—and send this data to cloud platform 402 for processingand storage. Cloud platform 402 can be any infrastructure that allowscloud services 412 to be accessed and utilized by cloud-capable devices.Cloud platform 402 can be a public cloud accessible via the Internet bydevices having Internet connectivity and appropriate authorizations toutilize the services 412. In some scenarios, cloud platform 402 can beprovided by a cloud provider as a platform-as-a-service (PaaS), and theservices 412 (such as the manifest system described herein) can resideand execute on the cloud platform 402 as a cloud-based service. In somesuch configurations, access to the cloud platform 402 and the services412 can be provided to customers as a subscription service by an ownerof the services 412. Alternatively, cloud platform 402 can be a privateor semi-private cloud operated internally by the enterprise, or a sharedor corporate cloud environment. An example private cloud can comprise aset of servers hosting the cloud services 412 and residing on acorporate network protected by a firewall.

Cloud services 412 can include, but are not limited to, data storage,data analysis, control applications (e.g., applications that cangenerate and deliver control instructions to industrial devices 408 and410 based on analysis of real-time system data or other factors),process simulation services, controller emulation services,visualization applications such as the cloud-based operator interfacesystem described herein, reporting applications, Enterprise ResourcePlanning (ERP) applications, notification services, or other suchservices. Cloud platform 402 may also include one or more object modelsto facilitate data ingestion and processing in the cloud. If cloudplatform 402 is a web-based cloud, cloud agents 406 at the respectiveindustrial facilities 404 may interact with cloud services 412 directlyor via the Internet. In an exemplary configuration, the industrialdevices 408 and 410 connect to the on-premise cloud agents 406 through aphysical or wireless local area network or radio link. In anotherexemplary configuration, the industrial devices 408 and 410 may accessthe cloud platform 402 directly using integrated cloud agents.

Ingestion of industrial device data in the cloud platform 402 throughthe use of cloud agents 406 can offer a number of advantages particularto industrial automation. For one, cloud-based storage offered by thecloud platform 402 can be easily scaled to accommodate the largequantities of data generated daily by an industrial enterprise, as wellas growing numbers of snapshot files generated by the snapshotmanagement architecture to be described in more detail below. Moreover,multiple industrial facilities at different geographical locations canmigrate their respective automation data to the cloud for aggregation,collation, collective analysis, visualization, simulation, andenterprise-level reporting without the need to establish a privatenetwork between the facilities. Cloud agents 406 can be configured toautomatically detect and communicate with the cloud platform 402 uponinstallation at any facility, simplifying integration with existingcloud-based data storage, analysis, or reporting applications used bythe enterprise. In another example application, cloud-based diagnosticapplications can monitor the health of respective automation systems ortheir associated industrial devices across an entire plant, or acrossmultiple industrial facilities that make up an enterprise. Cloud-basedlot control applications can be used to track a unit of product throughits stages of production and collect production data for each unit as itpasses through each stage (e.g., barcode identifier, productionstatistics for each stage of production, quality test data, abnormalflags, etc.). Moreover, cloud based control applications can performremote decision-making for a controlled industrial system based on datacollected in the cloud from the industrial system, and issue controlcommands to the system via the cloud agent. These industrialcloud-computing applications are only intended to be exemplary, and thesystems and methods described herein are not limited to these particularapplications. The cloud platform 402 can allow software vendors toprovide software as a service, removing the burden of softwaremaintenance, upgrading, and backup from their customers.

FIG. 5 is a block diagram of an example cloud-based operator trainingsystem 502 according to one or more embodiments of this disclosure.Aspects of the systems, apparatuses, or processes explained in thisdisclosure can constitute machine-executable components embodied withinmachine(s), e.g., embodied in one or more computer-readable mediums (ormedia) associated with one or more machines. Such components, whenexecuted by one or more machines, e.g., computer(s), computingdevice(s), automation device(s), virtual machine(s), etc., can cause themachine(s) to perform the operations described.

Cloud-based operator training system 502 can include an emulationcomponent 506, an emulation data exchange component 508, a simulationcomponent 510, a snapshot management component 512, a client interfacecomponent 514, one or more processors 516, and memory 518. In variousembodiments, one or more of the emulation component 506, emulation dataexchange component 508, simulation component 510, snapshot managementcomponent 512, client interface component 514, the one or moreprocessors 516, and memory 518 can be electrically and/orcommunicatively coupled to one another to perform one or more of thefunctions of the operator training system 502. In some embodiments,components 506, 508, 510, 512, and 514 can comprise softwareinstructions stored on memory 518 and executed by processor(s) 516.Operator training system 502 may also interact with other hardwareand/or software components not depicted in FIG. 5. For example,processor(s) 516 may interact with one or more external user interfacedevices, such as a keyboard, a mouse, a display monitor, a touchscreen,or other such interface devices.

Emulation component 506 can be configured to execute a virtualized oremulated industrial controller on a cloud platform. For example,emulation component 506 can comprise a soft controller engine that canbe programmed using standard industrial control programming software(e.g., a same programming platform used to program a hardware industrialcontroller), and can include an API layer that allows the controllerengine to interface with cloud data storage, process simulations, andon-premise hardware devices. Emulation data exchange component 508 canbe configured to provide connectivity between the emulation component'sAPI, cloud-based simulations, distributed on-premise simulations, and/orcloud services.

Simulation component 510 can be configured to execute cloud-basedsimulations that interact with the virtualized controller executed byemulation component 506. Snapshot management component 512 can beconfigured to capture and store process states of the cloud-basedsimulations and control states of the emulated controller, and toleverage these stored snapshots in connection with executing the processsimulation during a training session.

Client interface component 514 can be configured to exchange data withone or more client devices via an Internet connection. For example,client interface component 514 can deliver training interfaces toauthorized client devices that allow a user to interact with thecloud-based simulations.

The one or more processors 516 can perform one or more of the functionsdescribed herein with reference to the systems and/or methods disclosed.Memory 418 can be a computer-readable storage medium storingcomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed.

The cloud-based simulation and operator training system described hereincan leverages historical and/or real-time data collected into cloudstorage from on-premise industrial devices. This data can be used by thesystem, for example, in connection with modeling the industrial systemor process to be simulated on the cloud. Any suitable techniques formoving plant floor data to cloud storage for cloud-level analysis arewithin the scope of one or more embodiments of this disclosure. In someembodiments, a cloud agent architecture can be used to push industrialsystem data to the cloud platform. According to this cloud agentarchitecture, the industrial system data is collected by on-premisecloud agent devices, packaged into data packets, and pushed to the cloudplatform for remote viewing. FIG. 6 is an overview of a system thatleverages an agent-based cloud infrastructure to provide data collectionand processing services to customer manufacturing sites. This system canprovide remote collection and monitoring services in connection withcloud-based simulation and training.

In the example illustrated in FIG. 6, a data concentrator 628 collectsplant data from one or more industrial assets (e.g., data generated byone or more industrial controllers or associated I/O devices) at a plantfacility. These industrial assets can include industrial controllersthat monitor and control industrial I/O devices, data servers andhistorians, motor drives, remote I/O interfaces that remotely interfacegroups of I/O devices to one or more of the industrial controllers,boilers or other industrial machines, or other such assets. For example,data concentrator 528 can monitor one or more controller tags defined ina tag archive and store data in local data storage 636 (e.g., a localstructured query language, or SQL, server) associated with a historian638. The collected data can include historical data (e.g., alarmhistory, status history, trend data, etc.), live data values read fromthe industrial assets, alarm data generated by the industrial assets, orother types of data.

An on-premise cloud agent 640 is configured to collect the live orhistorical data from the industrial assets, either directly or byaccessing data storage 536 associated with data concentrator 628. Cloudagent 640 can execute on any suitable hardware platform (e.g., a server,a LINUX box, etc.), and acts as a generic gateway that collects dataitems from the various industrial assets on the plant network andpackages the collected data according to a generic, uniform datapackaging schema used to move the on-premise data to a cloud platform602. Cloud agent 640 provides a software mechanism to dynamically linkon-premise-to-cloud gateways. Cloud agent 640 provides an expandabledata type schema that allows new data types to be added without the needto redeploy the monitoring system to the cloud.

During data collection, the cloud agent 640 can intelligently sort andorganize the data based on defined criteria, including but not limitedto time of occurrence and/or user-defined priorities. Cloud agent 640can be, for example, a service (e.g., a Windows service) thatperiodically collects and transmits serialized and compressed data intothe cloud domain using standard web services over HTTPS/SSL.

FIG. 6 depicts data concentrator 628 as the data source for cloud agent640. This configuration can be useful if there are a large number ofdata points to monitor, since the data concentrator can 628 can linkmultiple industrial devices or other data sources to a single cloudagent 640. However, some embodiments of cloud agent 640 can collect datadirectly from the industrial assets themselves; e.g., through a commonindustrial protocol link, or through middleware applications such as OPCclients.

Cloud agent functionality is illustrated in more detail with referenceto FIG. 7. On-premise data collection is enabled by a collection ofservices that function as a virtual support engineer for processingdata. Data concentrator 628 and cloud agent 640 respectively implementtwo main functions associated with data collection—data concentrationusing a historian 638 and associated data storage 636 (e.g., an SQLserver), and cloud data enablement using cloud agent services executedby cloud agent 640. As noted above, plant data 710 is collected by dataconcentrator 628 at the plant facility. In an example scenario, plantdata 710 may comprise stamping press time series sensor data, made up ofthousands of data points updated at a rate of less than a second.

Collection services component 702 of cloud agent 640 implementscollection services that collect device data, either from dataconcentrator's associated data storage (e.g., via an SQL query) ordirectly from the devices themselves via a common industrial protocol(CIP) link or other suitable communication protocol. For example, toobtain data from data concentrator 628, collection services component702 may periodically run a data extraction query (e.g., an SQL query) toextract data from data storage 636 associated with data concentrator628. Collection services component 702 can then compress the data andstore the data in a compressed data file 712. Queue processing servicesexecuted by queue processing component 704 can then read the compresseddata file 712 and reference a message queuing database 714, whichmaintains and manage customer-specific data collection configurationinformation, as well as information relating to the customer'ssubscription to the cloud platform and associated cloud services. Basedon configuration information in the message queuing database 714, queueprocessing component 704 packages the compressed data file 712 into adata packet and pushes the data packet to the cloud platform. In someembodiments, the cloud agent 640 can support injecting data packets astorrential data 716.

Message queuing database 714 can include site-specific informationidentifying the data items to be collected (e.g., data tag identifiers),user-defined processing priorities for the data tags, firewall settingsthat allow cloud agent 640 to communicate with the cloud platformthrough a plant firewall, and other such configuration information.Configuration information in message queuing database 714 instructscloud agent 640 how to communicate with the identified data tags andwith the remote data collection services on the cloud platform.

In addition to collection and migration of data, one or more embodimentsof cloud agent 640 can also perform local analytics on the data prior tomoving the data to the cloud platform. This can comprise substantiallyany type of pre-processing or data refinement that may facilitateefficient transfer of the data to the cloud, prepare the data forenhanced analysis in the cloud, reduce the amount of cloud storagerequired to store the data, or other such benefits. For example, cloudagent 640 may be configured to compress the collected data using anysuitable data compression algorithm prior to migrating the data to thecloud platform. This can include detection and deletion of redundantdata bits, truncation of precision bits, or other suitable compressionoperations. In another example, cloud agent 640 may be configured toaggregate data by combining related data from multiple sources. Forexample, data from multiple sensors measuring related aspects of anautomation system can be identified and aggregated into a single cloudupload packet by cloud agent 640. Cloud agent 640 may also encryptsensitive data prior to upload to the cloud. In yet another example,cloud agent 640 may filter the data according to any specified filteringcriterion (e.g., filtering criteria defined in a filtering profilestored on the cloud agent). For example, defined filtering criteria mayspecify that pressure values exceeding a defined setpoint are to befiltered out prior to uploading the pressure values to the cloud.

In some embodiments, cloud agent 640 may also transform a specifiedsubset of the industrial data from a first format to a second format inaccordance with a requirement of a cloud-based analysis application. Forexample, a cloud-based reporting application may require measured valuesin ASCII format. Accordingly, cloud agent 640 can convert a selectedsubset of the gathered data from floating point format to ASCII prior topushing the data to the cloud platform for storage and processing.Converting the raw data at the industrial device before uploading to thecloud, rather than requiring this transformation to be performed on thecloud, can reduce the amount of processing load on the cloud side.

Cloud agent 640 may also associate metadata with selected subsets of thedata prior to migration to the cloud, thereby contextualizing the datawithin the industrial environment. For example, cloud agent 640 can tagselected subsets of the data with a time indicator specifying a time atwhich the data was generated, a quality indicator, a production areaindicator specifying a production area within the industrial enterprisefrom which the data was collected, a machine or process state indicatorspecifying a state of a machine or process at the time the data wasgenerated, a personnel identifier specifying an employee on duty at thetime the data was generated, or other such contextual metadata. In thisway, cloud agent 640 can perform layered processing of the collecteddata to generate meta-level knowledge that can subsequently be leveragedby cloud-based analysis tools to facilitate enhanced analysis of thedata in view of a larger plant context.

To ensure secure outbound traffic to the cloud, one or more embodimentsof cloud agent 640 can support HTTPS/SSL, certificate authority enabledtransmission, and/or unique identity using MAC addresses. Cloud agent640 can also support store-and-forward capability to ensure data is notlost if the agent becomes disconnected from the cloud.

Returning now to FIG. 6, cloud agent 640 sends compressed data packet624 to the cloud-based data collection and monitoring system on cloudplatform 602 via a cloud storage fabric 616. The data packet 624 conveysparameters and data (compressed and serialized) used by the cloud-sideservices to reconstruct the domain data structure in the cloud usingauxiliary tenant-level manifests. The cloud services direct remotestorage of the received data into preconditioned transient blobs 610.The cloud platform 602 can use agent reasoning and collective bargainfeatures to determine a data storage locale.

Through the configuration interface provided by cloud agent 640, usersat the plant facility can dynamically configure one or more priorityqueues 604 that respectively define how the data packets are processedin the cloud platform 602. For example, separate queues may be definedfor alarms, live data, and historical data, allowing data to beorganized according to these data types. The historical data queue canrelate to time-series records, which can be accessed through anapplication programming interface (API) (e.g., an SQL API or othersuitable API). The alarms queue can relate to abnormal situations, wherethe alarm data can also be accessed through the API. This alarms queuecan comprise multiple queues associated with different alarm priorities,to allow for individual processing for different alarms having differentlevels of criticality. In some embodiments, servers, controllers,switches, etc., can be monitored using a number of protocols, and at acertain point (e.g., at the end of a monitoring cycle) alarms can bequeued and cloud agent 640 can send the alarms to the cloud. Alarms canbe reactive (e.g., alarms that trigger when a motor fails, when a CPUcrashes, when an interlock is tripped, etc.) or proactive (e.g., amonitoring system may track consumables on a machine and generate analarm when time to reorder, monitor cycle counts on a machine andgenerate an alarm when to schedule preventative maintenance, generate analarm when temperatures fall outside defined bandwidths, send anotification when a computer's memory is 80% full, etc.).

The live data queue can relate to substantially real-time monitoreddata, such as current temperatures, current pressures, etc. The livedata values can also be accessed through the API (e.g., a SQL API). Thequeues described above are not intended to be limiting, and it is to beappreciated that other types of priority queues can be defined accordingto the needs of the end user. For example, queues may be defined forspecific devices or device types (e.g., motor drives) for uploading ofdevice parameter and/or performance data.

In some embodiments, cloud agent 640 can allow the user to define thesepriority queues 604 from the on-site location and to define how data ineach queue is handled. For example, the user can define, for each queue,an upload frequency, a priority level (e.g., which data queues shouldtake processing priority over other data queues), identities of cloudpartitions or databases in which data from the respective queues shouldbe stored, and other such information. In an example scenario, the livedata queue may be defined to process live data values that are to beused by a remote operator interface application to view substantiallyreal-time data from the plant facility, while historical data queue maybe used to process historian data for archival storage in a historicaldatabase on cloud storage. Accordingly, the live data queue may beassigned a higher priority relative to the historical data queue, sincedata in the live data queue is more time-critical than data in thehistorical queue.

Through cloud agent 640, users can assign priorities to respective datatags or tag groups at the customer site. These priority assignments canbe stored in the message queuing database 714 of the cloud agent 640.Accordingly, when queue processing component 704 packages the collecteddata to be moved to the cloud platform, the collected data items can bepackaged into data packets according to priority (as defined in messagequeuing database 714), and the respective data packet headers populatedwith the appropriate priority level. If access to the cloud isunavailable, data will continue to be collected by collection servicescomponent 702 and stored locally on the cloud agent in local storageassociated with collections services. When communication to the cloud isrestored, the stored data will be forwarded to cloud storage. Queueprocessing services can also encrypt and send storage account keys tothe cloud platform for user verification.

Message queuing services implemented by queue processing component 704of cloud agent 640 encapsulates or packages the compressed data file byadding customer-specific header information to yield a compressed datapacked (e.g., compressed data packet 624 of FIG. 8 described below). Forexample, the queue processing component 704 can access a message queuingdatabase (e.g., message queuing database 714 of FIG. 7), which storescustomer site configuration information and manages the customer'ssubscription to the cloud platform services. The message queuingdatabase may include such information as a customer identifierassociated with the customer entity associated with the industrialenterprise, a site identifier associated with a particular plantfacility from which the data was collected, a priority to be assigned tothe data (which may be dependent on the type of information being sent;e.g., alarm data, historical data, live operational data, etc.),information required to facilitate connection to the customer'sparticular cloud fabric, or other such information. The informationincluded in the header is based on this customer-specific informationmaintained in the message queuing database.

An example compressed data packet is illustrated in FIG. 8. As shown,the cloud agent's message queuing services add a header 804 tocompressed data file 712 to yield the compressed data packet 624. Theheader 804 contains customer-specific data read from message queuingdatabase 714. For example, header 804 can include a unique customeridentifier, a site identifier representing a particular plant facility,a virtual support engineer identifier, a data priority for the data inthe compressed data file 712, a message type, and a process identifierthat specifies a particular manifest application on the cloud platformthat should be used to process the data on the cloud side. Packaging thedata in this way can allow data from diverse data sources to be packagedtogether using a uniform, generic data packaging schema so that the datacan be moved to the cloud infrastructure.

When cloud agent 640 sends a data packet to the cloud-based remoteprocessing service, the service reads the packet's header information todetermine a priority assigned to the data (e.g., as defined in a datapriority field of the data packet) and sends the data packet (or thecompressed data therein) to a selected one of the user defined priorityqueues 604 based on the priority. On the other side of the priorityqueues 604, a data process service 608 processes data in the respectivepriority queues 604 according to the predefined processing definitions.The data processing service includes a worker role 632 that determineshow the queued data is to be processed based on manifests (e.g., systemmanifests, tag manifests, and metric manifests) stored in acustomer-specific manifest assembly 634. Manifests define and implementcustomer-specific capabilities, applications, and preferences forprocessing collected data in the cloud. Manifests can be dynamicallyuploaded by a user at the plant facility through cloud agent 640, whichfacilitates dynamic extension of cloud computing capability.

For example, if new data points are to be added to the data collectionsystem that require creation of a new data queue, the user can interactwith cloud agent 640 to configure a new manifest for the new queue, themanifest defining such aspects as processing priority for the data,upload frequency for the data, where the data is to be routed or storedwithin cloud storage, and other such information. Cloud agent 640 canthen upload the new manifest 606 together with the data (orindependently of the data). The new manifest 606 is then added to thecustomer's manifest assembly 634 with the other manifests defined forthe customer, so that worker role 632 can leverage the new manifest 606to determine how data in the new queue is to be processed. This newmanifest 606 need only be uploaded to the cloud-based remote monitoringservice once. Thereafter, data placed in the new priority queue will beprocessed by worker role 632 according to the new manifest 606 stored inthe customer's manifest assembly 634. For example, the manifest maydefine where the data is to be stored within cloud storage (e.g., in ahistorical database, and Alarms and Live Data database, big data storage612, etc.), and whether processing of the new data queue is to takepriority over other data queues. In some embodiments, the manifestassembly 634 may only accept a new manifest if the manifest isaccompanied by a unique key associated with the client.

Once the cloud-based infrastructure has processed and stored the dataprovided by cloud agent 640 according to the techniques described above,the data can be processed by cloud-based services executing on the cloudplatform, such as the cloud-based simulation and operator trainingsystem described herein. For example, as will be described in moredetail herein, the operator training system (OTS) services 614 canleverage the stored data in connection with generating a high-fidelitysimulation of a physical plant-floor industrial system or process. TheOTS services 614 can also execute an industrial controller emulationthat emulates control of the industrial automation system or process bythe physical controller located on the plant floor. The OTS services 614can serve OTS interfaces 642 to one or more client devices 620. Theseinterfaces 642 allow a user to interact with the cloud-based simulationin the context of an operator training scenario.

FIG. 9 is a diagram illustrating runtime of a cloud-based simulation andemulation system that includes a snapshot management component forcreation of operator training scenarios. According to one or moreembodiments, the emulation component of the cloud-based operatortraining system can execute a virtualized controller 918 on the cloudplatform. The virtualized controller 918 is driven by a controllerengine that serves as a core component of the cloud-based operatortraining system, and runs on an industrial controller emulation platformthat allows the virtualized controller 918 to be programmed using thesame programming tools and programming platforms used to programhardware controllers (e.g., ladder logic, sequential function chart,structured text, etc.). This allows the virtualized controller 918 to beprogrammed and configured by plant engineers, rather than requiring theservices of a data scientist or cloud analytics specialist.

The simulation component 510 of the cloud-based emulation and analyticssystem can also execute a cloud-based simulation 904 that models one ormore aspects of an industrial system (e.g., an industrial machine orprocess, a work area, etc.). Simulation 904 interacts with virtualizedcontroller 918 in order to simulate control of the modeled machine orprocess by the control program executed by virtualized controller 918.Coordinated interaction between simulation 904 and virtualizedcontroller 918 is achieved using an emulation data exchange interface(EDEI) of the emulation runtime engine on the cloud platform. In theexample depicted in FIG. 9, simulation 904 models an industrial systemor process for which training is to be provided. The simulation 904 maybe generated and maintained by a model building application 908 thatexecutes on workstation 902, which may be any suitable on-premisecomputing device (e.g., a desktop, laptop, or tablet computer, etc.). Onthe cloud platform, emulation services 910 provided by the emulationcomponent execute virtualized controller 918. Simulation 904 exchangesinformation with virtualized controller 918 via the EDEI service 912.The EDEI service 912 provides connectivity among the application programinterfaces of the cloud emulation services 910, simulation 904, andother cloud services executing on the cloud platform.

EDEI service 912 includes a tag server 914 that maps simulated I/O databetween simulation 904 and virtualized controller 918. EDEI service 912can also map I/O data for streaming between the cloud platform andon-premise devices (e.g., industrial devices making up the physicalindustrial system or process on the plant floor). In this example, tagserver 914 defines data to be exchanged between simulation 904 and thevirtualized controller 918 executed by the cloud emulation services 910.However, the tag server 914 can also define data mappings betweenvirtualized controller 918 and other devices, including but not limitedto on-premise industrial controllers or other industrial devices. Tofacilitate mapping between data points of simulation 904 and virtualizedI/O points of the virtualized controller 918, the user may provideinformation regarding the simulation model—e.g., an MDL file 924—to theEDEI service 912. Tag server 914 may use information contained in theMDL file 924 to link simulation I/O points with I/O points of thevirtualized controller. Based on the I/O data mapping defined by the tagserver 914, the EDEI service 912 will exchange the defined data itemsbetween simulation 904 and virtualized controller 918.

A functional mock-up unit (FMU) is established by exchange of FMU files920 between EDEI service 912 and simulation 904. Virtualized controller918 can be programmed remotely using workstation 902, which executes astandard industrial controller programming platform (e.g., a ladderlogic development platform). In particular, a user at workstation 902can develop a control program and send the compiled control program 922to the EDEI service 912, which passes the control program to thevirtualized controller for execution.

During runtime, tag server 914 exchanges data between simulation 904 andvirtualized controller 918 on the cloud platform. The EDEI service 912may also leverage cloud storage 916 to retrieve or store data relatingto the simulation session. The simulation session may be monitored by auser via a dashboard or OTS interface (e.g., OTS interface 642 shown inFIG. 6) or other graphical interface served to the user's client device.The OTS interface can also provide interactive controls that allow theuser to interact with the simulation 904 and virtualized controller 918in the context of an operator training scenario.

In order to accurately simulate behaviors and responses of the physicalindustrial system or process being modeled by simulation 904, thecloud-based OTS system can leverage previously captured states of boththe simulated system and the emulated virtual controller. This stateinformation can be stored on cloud storage 916 in the form of snapshotfiles 926, and used by the cloud-based OTS system during a trainingsimulation in order to accurately recreate a state or behavior of thesimulated system in response to an operator action, and further in viewof a current state of the virtualized controller 918 (e.g., the currentvalue of the various I/O points, a current state of the program beingexecuted by the virtualized controller 918, etc.). Fidelity of thesimulation improves as the number of snapshot files 926 increases. Inorder to generate a large collection of snapshot files 926 for use bythe OTS system, the system includes a snapshot management component 512configured to capture and store states of the simulation 904 andcorresponding states of the virtualized controller 918 during a trainingsession.

FIG. 10 illustrates generation and storage of snapshot files by snapshotmanagement component 512. In response to a defined trigger conditionduring a simulation and/or training session (to be described in moredetail below), the snapshot management component 502 captures a processstate 1004 of the simulation 904. This process state 1004 represents oneor more states of the simulated industrial system or process at the timeof the trigger condition, and may include simulated status of one ormore industrial devices or components comprising the simulation (e.g., apressure, a temperature, a position, a speed, an alarm condition, etc.).The snapshot management component also captures a corresponding controlstate 1006 of the virtualized controller 918 at the time of the trigger.The control state 1006 can represent the values of the virtualizedcontroller's I/O at the time of the trigger (e.g., analog and digitalinput and outputs), as well as the state of the control program orroutine being executed by the virtualized controller. The snapshotmanagement component 502 records the process state and its correspondingcontrol state 1006 into a snapshot file 1002 and stores the file oncloud storage 916. The system continues to generate and store a growingcollection of snapshot files for the simulated system over the course ofmultiple simulation executions.

FIG. 11 is a flow diagram illustrating an example sequence of eventscarried out by the OTS system described herein for generating andstoring snapshot files. When an operator training session is initiated,the system first configures the simulation testbed at 1104. This caninvolve, for example, loading the appropriate machine simulation andvirtualized controller, configuring the I/O data exchange between thesimulation and the virtualized controller (e.g., by configuring the tagserver 914 of the EDEI service 912), retrieving any snapshot filespreviously generated for the simulation from cloud storage 916, or otherpreliminary system configuration steps. The system then configuresvirtualized controller 918 and simulation 904 by downloading the controlapplication to the virtualized controller 918 at 1106, and downloadingthe simulation and 3D animation to the simulation 904 at 1108. Controlstates defined by the snapshot files are then downloaded to thevirtualized controller 918 at 1110. At 1112, a functional mock-up unit(FMU) file, which establishes a communication interface betweensimulation 904 and virtualized controller 918, is downloaded to thesimulation 904.

Once the virtualized controller 918 and simulation (with associated 3Dgraphics) are configured, a determination is made at 1121 whether thesimulation is to be run. If so, the simulation is run at 1114(otherwise, the OTS session ends). During the simulation, the OTS systemsimulates control of the machine or process (modeled by simulation 904)by the industrial controller represented by the virtualized controller918. During this time, users can interact with the simulated system viaOTS interfaces served to a workstation or other client device by the OTSsystem. The OTS interface allow the user to simulate interaction withand operation of the machine or process represented by simulation 904.The interface also provides graphical and/or alphanumeric feedback tothe user indicating the state or behavior of the machine and/or thecontroller in response to the simulated operator interactions. This caninclude, for example, graphically conveying the behavior of the machinevia a 3D graphic representation.

Prior to or during the simulation, a stop condition is set at 1118. Thisstop condition defines a system state at which the simulation generatesa snapshot file. The condition can define one or more simultaneousconditions of the process and/or control state that must be true inorder to trigger generation of the snapshot file. An example conditionmay specify, for example, that the snapshot is to be taken when aspecified pressure reaches a defined setpoint value, and a specifiedtemperature falls below a defined value.

When it is determined at 1116 that the condition set at step 1118 hasbeen reached, the process state of the simulation 904 and thecorresponding control state of the virtualized controller 918 iscaptured by the snapshot management component, which creates a snapshotfile at 1120 recording these states. The resulting snapshot file is thenstored in cloud storage 916 along with previously collected snapshotfiles for the simulated system. As noted above, these snapshot files arereferenced by the OTS system to accurately model the behavior of thesimulated machine or process under different operating conditions or inresponse to different operator interactions.

FIG. 12 is a diagram of an architecture for distributing OTS interfacesto user workstations to facilitate training interaction with thecloud-based simulation. The cloud-based OTS system includes a clientinterface component 514 that can serve OTS virtual machines 1204 to oneor more workstations 1202 or other client devices. Workstations 1202 maycomprise, for example, training workstations located in a training room.Since the cloud-based operator training system resides and executes onthe cloud platform, multiple workstations or client devices can interactwith the simulation simultaneously. The client interface component 514can serve the OTS virtual machines to authorized users via theworkstations or other internet-capable personal device.

The OTS system architecture also includes a snapshot management portalcomponent 1208, which acts as an administrative and design portalthrough which an administrator (via an administrator client device 1206,such as a desktop computer, a laptop computer, a tablet computer, apersonal device with internet capability, etc.) can manage the snapshotdatabase and design training scenarios using the snapshot data. Forexample, the snapshot management portal component 1208 can serve anadministration dashboard or interface to the administrator client device1206 that allows the administrator to configure a training classroom byselecting one or more operating scenarios represented by correspondingsubsets of the snapshot files 926. The selected scenarios will then beused by the system for the training session.

During the training session, the administrator—through the snapshotmanagement portal component 1208—can instruct the OTS system to load theselected operating scenarios onto the OTS virtual machines 1204, whichare then delivered to the training workstations 1202. The OTS virtualmachines 1204 are instances of template virtual machines on the cloudplatform that have been configured in accordance with the selectedoperating scenarios. That is, the system loads the template virtualmachines with instances of the simulation 904, the emulated virtualizedcontroller 918, the snapshot files corresponding to the selectedoperating scenarios, and any necessary synchronization units forsynchronizing the simulation 904 with the virtualized controller 918.Client interface 514 then delivers instances of these configured virtualmachines 1204 to workstations 1202.

The OTS virtual machines 1204 allow users at the workstations 1202 toview and interact with the simulation and virtualized controller 918running the selected operating scenario (driven by the subset of thesnapshot files 926 corresponding to the selected operating scenario).FIG. 13 is a diagram illustrating exchange of OTS data 1302 between thecloud-based OTS system and workstation 1202. OTS interfaces 1204 canrender animated 3D graphics representing the machine or process beingsimulated by simulation 904. These graphical representations can conveya visual representation of the machine's current simulated state. TheOTS interfaces also receive operator input via the workstations 1202representing operator interactions with the simulated machine orprocess, including but not limited to input representing a control panelinteraction (e.g., interaction with a push button, interaction with aselector switch, changing of a setpoint value, etc.), a change to thecontrol program executing on the virtualized controller, or other suchinteractions. The machine state information conveyed to the users viathe OTS interfaces 1204 are based in part on the snapshot files capturedaccording to the process described above in connection with FIG. 11.EDEI service 912 provides connectivity between simulation 904,virtualized controller 918, and cloud storage 916, which stores capturedsnapshot files as well as data collected from the industrial system(s)by the tag server of the EDEI service.

In some embodiments, the OTS training system can organize the storedsnapshot files according to industry- and/or customer-specificcategories to facilitate simplified management and location of thedesired subset of snapshots corresponding to a selected operatingscenario. FIG. 14 illustrates an example, non-limiting organizationalschema 1400 for industry-based categorization of snapshot files. It isto be appreciated, however, that schema 1400 is only intended to beexemplary, and that substantially any snapshot classification schema iswithin the scope of this disclosure.

As new snapshot files are generated by the OTS system, snapshotmanagement component 512 can classify the snapshots according to anindustry category 1402, a customer type category 1404, and/or operatingscenarios 1406. In some embodiments, these classifications can beorganized hierarchically, as shown in FIG. 14. Example industrycategories can include, but are not limited to, automotive,pharmaceutical, food and beverage, oil and gas, marine, fibers andtextiles, mining, power generation, life sciences, pulp and paper, orother such industries. Snapshot management component 915 can classifysnapshot files 1408 in cloud storage according to these variousindustrial categories. Each industry category can be subdivided intosub-categories corresponding to customer types within each industry, andeach customer type can be associated with one or more simulationscenarios relevant to the corresponding industry and customer type. Thesnapshot management portal component 1208 can allow an administrator orother user to navigate this hierarchical classification schema in orderto locate desired training scenarios within each industry, and toidentify the sub-set of snapshot files 1408 corresponding to theselected scenario.

The cloud-based architecture for process simulation, controlleremulation, and snapshot management described above can also be used forother functions instead of or in addition to operator training. Forexample, since the large collection of snapshot files generated andstored by the system captures a wide range of machine or processbehaviors, scenario analysis can be performed on this snapshotinformation by a big data analysis system executing on the cloudplatform. Such analysis could identify—based on the collection ofobserved system behavior in response to different operator interactionsand within different operating scenarios—system parameters that could bemodified to improve performance of the physical machine or system,common operator behaviors that result in substandard machine operation,or other such learned information. Cloud-based troubleshooting systemscould also access the snapshot data in connection with troubleshooting aperformance issue with the physical machine or process, in order toidentify possible solutions or maintenance recommendations forcorrecting the issue or mitigating future occurrences of the issue.

In another example embodiment, the control and process state informationrecorded in the snapshot files can be used to train an artificialintelligence system to understand, operate, and/or troubleshoot thephysical machine or process represented by the simulation. In suchembodiments, the artificial intelligence system can interact with thecloud simulation to simulate operator interactions with the machine orprocess represented by the simulation. Such interactions can be similarto those carried out by the human trainees via the OTS virtual machines1204 during a training session. However, in this scenario the artificialintelligence system—rather than the human operator—decides whichoperator interactions to carry out in response to a given operatingscenario presented by the cloud-based OTS system, and observes thesimulated machine response to those interactions. In this way, theartificial intelligence system can learn correct operation of a physicalmachine or process in the same manner as a human user of the OTS system.

Embodiments, systems, and components described herein, as well asindustrial control systems and industrial automation environments inwhich various aspects set forth in the subject specification can becarried out, can include computer or network components such as servers,clients, programmable logic controllers (PLCs), automation controllers,communications modules, mobile computers, wireless components, controlcomponents and so forth which are capable of interacting across anetwork. Computers and servers include one or more processors—electronicintegrated circuits that perform logic operations employing electricsignals—configured to execute instructions stored in media such asrandom access memory (RAM), read only memory (ROM), a hard drives, aswell as removable memory devices, which can include memory sticks,memory cards, flash drives, external hard drives, and so on.

Similarly, the term PLC or automation controller as used herein caninclude functionality that can be shared across multiple components,systems, and/or networks. As an example, one or more PLCs or automationcontrollers can communicate and cooperate with various network devicesacross the network. This can include substantially any type of control,communications module, computer, Input/Output (I/O) device, sensor,actuator, and human machine interface (HMI) that communicate via thenetwork, which includes control, automation, and/or public networks. ThePLC or automation controller can also communicate to and control variousother devices such as standard or safety-rated I/O modules includinganalog, digital, programmed/intelligent I/O modules, other programmablecontrollers, communications modules, sensors, actuators, output devices,and the like.

The network can include public networks such as the internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, and Ethernet/IP. Othernetworks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, CAN, wireless networks, serial protocols, and so forth. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 15 and 16 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 15, an example environment 1510 for implementingvarious aspects of the aforementioned subject matter includes a computer1512. The computer 1512 includes a processing unit 1514, a system memory1516, and a system bus 1518. The system bus 1518 couples systemcomponents including, but not limited to, the system memory 1516 to theprocessing unit 1514. The processing unit 1514 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit1514.

The system bus 1518 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 1516 includes volatile memory 1520 and nonvolatilememory 1522. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1512, such as during start-up, is stored in nonvolatile memory 1522. Byway of illustration, and not limitation, nonvolatile memory 1522 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 1520 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM(SLDRAM), and direct Rambus RAM (DRRAM).

Computer 1512 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 15 illustrates, forexample a disk storage 1524. Disk storage 1524 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1524 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1524 to the system bus 1518, a removableor non-removable interface is typically used such as interface 1526.

It is to be appreciated that FIG. 15 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 1510. Such software includes an operatingsystem 1528. Operating system 1528, which can be stored on disk storage1524, acts to control and allocate resources of the computer 1512.System applications 1530 take advantage of the management of resourcesby operating system 1528 through program modules 1532 and program data1534 stored either in system memory 1516 or on disk storage 1524. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 1512 throughinput device(s) 1536. Input devices 1536 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1514through the system bus 1518 via interface port(s) 1538. Interfaceport(s) 1538 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1540 usesome of the same type of ports as input device(s) 1536. Thus, forexample, a USB port may be used to provide input to computer 1512, andto output information from computer 1512 to an output device(s) 1540.Output adapters 1542 are provided to illustrate that there are someoutput devices 1540 like monitors, speakers, and printers, among otheroutput devices 1540, which require special adapters. The output adapters1542 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1540and the system bus 1518. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1544.

Computer 1512 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1544. The remote computer(s) 1544 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1512. For purposes of brevity, only a memory storage device 1546 isillustrated with remote computer(s) 1544. Remote computer(s) 1544 islogically connected to computer 1512 through a network interface 1548and then physically connected via communication connection(s) 1550.Network interface 1548 encompasses communication networks such aslocal-area networks (LAN) and wide-area networks (WAN). LAN technologiesinclude Fiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 1550 refers to the hardware/softwareemployed to connect the network interface 1548 to the system bus 1518.While communication connection(s) 1550 is shown for illustrative clarityinside computer 1512, it can also be external to computer 1512. Thehardware/software necessary for connection to the network interface 1548includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 16 is a schematic block diagram of a sample computing environment1600 with which the disclosed subject matter can interact. The samplecomputing environment 1600 includes one or more client(s) 1602. Theclient(s) 1602 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 1600also includes one or more server(s) 1604. The server(s) 1604 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 1604 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 1602 and servers 1604 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 1600 includes acommunication framework 1606 that can be employed to facilitatecommunications between the client(s) 1602 and the server(s) 1604. Theclient(s) 1602 are operably connected to one or more client datastore(s) 1608 that can be employed to store information local to theclient(s) 1602. Similarly, the server(s) 1604 are operably connected toone or more server data store(s) 1610 that can be employed to storeinformation local to the servers 1604.

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe disclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

1. A system, comprising: a memory that stores executable operations; anda processor, operatively coupled to the memory, that executes theexecutable operations, the executable operations comprising: executing avirtualized industrial controller on a cloud platform; executing asimulation of one or more devices or process of an industrial system onthe cloud platform to generate a simulated industrial system; executingan emulation data exchange interface that communicatively connects thevirtualized controller and the simulated industrial system, wherein thesimulated industrial system is controlled by the virtualized controllerthrough the emulation data exchange interface; and determining whetherone or more defined conditions of the simulation have been met; inresponse to determining that the one or more defined conditions of thesimulation have been met, capturing process state data representing oneor more states of the simulated system associated with the time of theone or more defined conditions being met and control state datarepresenting input/output values of the virtualized industrialcontroller from/to the simulated industrial system associated with thetime of the one or more defined conditions being met, and generatingsimulation files to include the process state data and the control statedata.
 2. The system of claim 1, wherein the one or more definedconditions comprise simultaneous conditions of the one or more devicesor process of the industrial system.
 3. The system of claim 1, whereinthe one or more defined conditions comprise a condition in which atemperature represented by the simulation falls below a temperaturethreshold value.
 4. The system of claim 1, wherein the one or moredefined conditions comprise a condition in which a pressure representedby the simulation reaches a pressure threshold value.
 5. The system ofclaim 1, wherein the process state data records a simulated status of asimulated industrial device at a time that one of the one or moredefined conditions of the simulation was met.
 6. The system of claim 1,wherein the control state data represents a state of a control programbeing executed by the virtualized industrial controller at a time thatone of the one or more defined conditions of the simulation was met. 7.The system of claim 1, wherein the executable operations furthercomprises capturing new process state data representing a new processstate of the simulation and new control state data representing a newcontrol state of the virtualized industrial controller based on operatorinput and a response of the simulated industrial system to the operatorinput, wherein the operator input represents an operator interactionwith the simulated industrial system.
 8. The system of claim 1, whereinthe executable operations further comprises modeling behaviors of thesimulated industrial system under different operating conditions or inresponse to different operator interactions using the simulation files.9. A method for industrial simulation, comprising: executing, by one ormore cloud platform devices comprising at least one processor, avirtualized industrial controller; executing, by the one or more cloudplatform devices, a simulation of one or more devices or process of anindustrial system on the cloud platform to generate a simulatedindustrial system; executing, by the one or more cloud platform devices,an emulation data exchange interface that communicatively connects thevirtualized controller and the simulated industrial system, wherein thesimulated industrial system is controlled by the virtualized controllerthrough the emulation data exchange interface; and determining, by theone or more cloud platform devices, whether one or more definedconditions of the simulation have been met; in response to determiningthat the one or more defined conditions of the simulation have been met,capturing, by the one or more cloud platform devices, process state datarepresenting one or more states of the simulated system associated withthe time of the one or more defined conditions being met and controlstate data representing input/output values of the virtualizedindustrial controller from/to the simulated industrial system associatedwith the time of the one or more defined conditions being met, andgenerating, by the one or more cloud platform devices, simulation filesto include the process state data and the control state data.
 10. Themethod of claim 9, wherein the one or more defined conditions comprisesimultaneous conditions of the one or more devices or process of theindustrial system.
 11. The method of claim 9, wherein the one or moredefined conditions comprise a condition in which a temperaturerepresented by the simulation falls below a temperature threshold value.12. The method of claim 9, wherein the one or more defined conditionscomprise a condition in which a pressure represented by the simulationreaches a pressure threshold value.
 13. The method of claim 9, whereinthe process state data records a simulated status of a simulatedindustrial device at a time that one of the one or more definedconditions of the simulation was met.
 14. The method of claim 9, whereinthe control state data represents a state of a control program beingexecuted by the virtualized industrial controller at a time that one ofthe one or more defined conditions of the simulation was met.
 15. Themethod of claim 9, further comprising capturing, by the one or morecloud platform devices, new process state data representing a newprocess state of the simulation and new control state data representinga new control state of the virtualized industrial controller based onoperator input and a response of the simulated industrial system to theoperator input, wherein the operator input represents an operatorinteraction with the simulated industrial system.
 16. The method ofclaim 9, further comprising modeling, by the one or more cloud platformdevices, behaviors of the simulated industrial system under differentoperating conditions or in response to different operator interactionsusing the simulation files.
 17. A non-transitory computer-readablemedium having stored thereon executable instructions that, in responseto execution, cause a system comprising a processor to performoperations, the operations comprising: executing a virtualizedindustrial controller on a cloud platform; executing a simulation of oneor more devices or process of an industrial system on the cloud platformto generate a simulated industrial system; executing an emulation dataexchange interface that communicatively connects the virtualizedcontroller and the simulated industrial system, wherein the simulatedindustrial system is controlled by the virtualized controller throughthe emulation data exchange interface; and determining whether one ormore defined conditions of the simulation have been met; in response todetermining that the one or more defined conditions of the simulationhave been met, capturing process state data representing one or morestates of the simulated system associated with the time of the one ormore defined conditions being met and control state data representinginput/output values of the virtualized industrial controller from/to thesimulated industrial system associated with the time of the one or moredefined conditions being met, and generating simulation files to includethe process state data and the control state data.
 18. Thenon-transitory computer-readable medium of claim 17, wherein the one ormore defined conditions comprise simultaneous conditions of the one ormore devices or process of the industrial system.
 19. The non-transitorycomputer-readable medium of claim 17, wherein the one or more definedconditions comprise a condition in which a temperature represented bythe simulation falls below a temperature threshold value.
 20. Thenon-transitory computer-readable medium of claim 17, wherein the one ormore defined conditions comprise a condition in which a pressurerepresented by the simulation reaches a pressure threshold value.